Associating Peritoneal Metastasis With T2‐Weighted MRI Images in Epithelial Ovarian Cancer Using Deep Learning and Radiomics: A Multicenter Study

Background The preoperative diagnosis of peritoneal metastasis (PM) in epithelial ovarian cancer (EOC) is challenging and can impact clinical decision‐making. Purpose To investigate the performance of T2‐weighted (T2W) MRI‐based deep learning (DL) and radiomics methods for PM evaluation in EOC patie...

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Bibliographic Details
Published in:Journal of magnetic resonance imaging 2024-01, Vol.59 (1), p.122-131
Main Authors: Wei, Mingxiang, Zhang, Yu, Ding, Cong, Jia, Jianye, Xu, Haimin, Dai, Yao, Feng, Guannan, Qin, Cai, Bai, Genji, Chen, Shuangqing, Wang, Hong
Format: Article
Language:English
Subjects:
Cancer
Carcinoma, Ovarian Epithelial - diagnostic imaging
Decision making
Deep Learning
Female
Field strength
Humans
Magnetic Resonance Imaging
Mathematical models
Metastases
Metastasis
Ovarian cancer
Ovarian Neoplasms - diagnostic imaging
peritoneal metastasis
Peritoneal Neoplasms
Peritoneum
Population studies
Radiology
Radiomics
Retrospective Studies
Sensitivity
Statistical analysis
Statistical tests
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Summary:Background The preoperative diagnosis of peritoneal metastasis (PM) in epithelial ovarian cancer (EOC) is challenging and can impact clinical decision‐making. Purpose To investigate the performance of T2‐weighted (T2W) MRI‐based deep learning (DL) and radiomics methods for PM evaluation in EOC patients. Study Type Retrospective. Population Four hundred seventy‐nine patients from five centers, including one training set (N = 297 [mean, 54.87 years]), one internal validation set (N = 75 [mean, 56.67 years]), and two external validation sets (N = 53 [mean, 55.58 years] and N = 54 [mean, 58.22 years]). Field Strength/Sequence 1.5 or 3 T/fat‐suppression T2W fast or turbo spin‐echo sequence. Assessment ResNet‐50 was used as the architecture of DL. The largest orthogonal slices of the tumor area, radiomics features, and clinical characteristics were used to construct the DL, radiomics, and clinical models, respectively. The three models were combined using decision‐level fusion to create an ensemble model. Diagnostic performances of radiologists and radiology residents with and without model assistance were evaluated. Statistical Tests Receiver operating characteristic analysis was used to assess the performances of models. The McNemar test was used to compare sensitivity and specificity. A two‐tailed P 
ISSN:1053-1807
1522-2586
DOI:10.1002/jmri.28761